Breast cancer risk has conventionally been assessed using family history (FH) and rare high/moderate penetrance pathogenic variants (PVs), notably in BRCA1/2, and more recently PALB2, CHEK2, and ATM. In addition to these PVs, it is now possible to use increasingly predictive polygenic risk scores (PRS) as well. The comparative population-level predictive capability of these three different indicators of genetic risk for risk stratification is, however, unknown.
TheCanadian heritable breast cancer risk distribution was estimated using a novel genetic mixing model (GMM). A realistically representative sample of women was synthesized based on empirically observed demographic patterns for appropriately correlated family history, inheritance of rare PVs, PRS, and residual risk from an unknown polygenotype. Risk assessment was simulated using the BOADICEA risk algorithm for 10-year absolute breast cancer incidence, and compared to heritable risks as if the overall polygene, including its measured PRS component, and PV risks were fully known.
Generally, the PRS was most predictive for identifying women at high risk, while family history was the weakest. Only the PRS identified any women at low risk of breast cancer.
PRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.
PRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.The availability of genetic test data within the electronic health record (EHR) is a pillar of the US vision for an interoperable health IT infrastructure and a learning health system. Although EHRs have been highly investigated, evaluation of the information systems used by the genetic labs has received less attention-but is necessary for achieving optimal interoperability. This study aimed to characterize how US genetic testing labs handle their information processing tasks.
We followed a qualitative research method that included interviewing lab representatives and a panel discussion to characterize the information flow models.
Ten labs participated in the study. We identified three generic lab system models and their relevant characteristics a backbone system with additional specialized systems for interpreting genetic results, a brokering system that handles housekeeping and communication, and a single primary system for results interpretation and report generation.
Labs have heterogeneous workflows and generally have a low adoption of standards when sending genetic test reports back to EHRs. Core interpretations are often delivered as free text, limiting their computational availability for clinical decision support tools. Increased provision of genetic test data in discrete and standard-based formats by labs will benefit individual and public health.
Labs have heterogeneous workflows and generally have a low adoption of standards when sending genetic test reports back to EHRs. Core interpretations are often delivered as free text, limiting their computational availability for clinical decision support tools. Increased provision of genetic test data in discrete and standard-based formats by labs will benefit individual and public health.The ClinGen Variant Curation Expert Panels (VCEPs) provide disease-specific rules for accurate variant interpretation. Using the hearing loss-specific American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines, the Hearing Loss VCEP (HL VCEP) illustrates the utility of expert specifications in variant interpretation.
A total of 157 variants across nine HL genes, previously submitted to ClinVar, were curated by the HL VCEP. The curation process involved collecting published and unpublished data for each variant by biocurators, followed by bimonthly meetings of an expert curation subgroup that reviewed all evidence and applied the HL-specific ACMG/AMP guidelines to reach a final classification.
Before expert curation, 75% (117/157) of variants had single or multiple variants of uncertain significance (VUS) submissions (17/157) or had conflicting interpretations in ClinVar (100/157). https://www.selleckchem.com/products/epacadostat-incb024360.html After applying the HL-specific ACMG/AMP guidelines, 24% (4/17) of VUS and 69% (69/100) of discordant variants were resolved into benign (B), likely benign (LB), likely pathogenic (LP), or pathogenic (P). Overall, 70% (109/157) variants had unambiguous classifications (B, LB, LP, P). We quantify the contribution of the HL-specified ACMG/AMP codes to variant classification.
Expert specification and application of the HL-specific ACMG/AMP guidelines effectively resolved discordant interpretations in ClinVar. This study highlights the utility of ClinGen VCEPs in supporting more consistent clinical variant interpretation.
Expert specification and application of the HL-specific ACMG/AMP guidelines effectively resolved discordant interpretations in ClinVar. This study highlights the utility of ClinGen VCEPs in supporting more consistent clinical variant interpretation.Experimental and clinical studies have shown that vitamins A and E can inhibit cancer formation and progression. The unfavourable status of these vitamins can represent risk factors for the disease. This study aimed to evaluate the associations between the nutritional status of vitamins A and E (serum levels and dietary intake) and histopathological outcomes in Papillary Thyroid Carcinoma (PTC) patients.
We applied a cross-sectional study (2017-2018) and quantified retinol (ROH) and α-tocopherol (TOH) serum levels and vitamins dietary intake of 46 PTC patients. Serum vitamins were quantified by high efficiency liquid chromatography and vitamins dietary intake was analyzed by 24-hr dietary recalls.
Patients with lower ROH serum levels were more likely to present lymph node metastasis and/or angiolymphatic invasion (p?=?0.025). In addition, higher vitamin A and vitamin E intake are related to the absence of extrathyroidal extension (p?=?0.013) and lymph node metastasis (p?=?0.007), respectively. Our findings suggest that a ROH serum level greater than 2.